Challenge: Prior-message context provides the greatest lift in Teams (chat) scenario.
Approach: They compare prior-message context with email and chat messages from Microsoft Teams and Outlook.
Outcome: The proposed model outperforms existing models on two of the largest commercial communication platforms: Microsoft Teams and Outlook.

Similar Papers

On the Role of Context in Reading Time Prediction (2024.emnlp-main)

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Challenge: a new perspective on how readers integrate context during reading time prediction is presented . a recent study shows that the proportion of variance in reading times explained by context is smaller when context is represented by the orthogonalized predictor.
Approach: They propose a technique where they project surprisal onto the orthogonal complement of frequency.
Outcome: The proposed method shows that the proportion of variance in reading times explained by context is smaller when context is represented by the orthogonalized predictor.
Predicting Reference: What do Language Models Learn about Discourse Models? (2020.emnlp-main)

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Challenge: a growing literature that probes neural language models to assess their latent acquisition of grammatical knowledge has not investigated their acquisition of discourse modeling ability.
Approach: They draw on a psycholinguistic literature that has established how different contexts affect referential biases concerning who is likely to be referred to next.
Outcome: The proposed models do not resemble human language users, the authors show . their models capture the linguistic knowledge required to perform discourse modeling .
Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers (P18-2)

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Challenge: cloze deletion test is a test that requires the learner to understand the context and vocabulary in order to identify the correct word.
Approach: They collect data from human participants and test various models in a local and a global context condition to examine the role of linguistic context in predicting quantifiers.
Outcome: The proposed models outperform humans in a local and global context and are only slightly better in the latter.
An Empirical Investigation of Contextualized Number Prediction (2020.emnlp-main)

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Challenge: a large scale empirical investigation of contextualized number prediction in running text is needed.
Approach: They propose a suite of output distribution parameterizations that incorporate latent variables to add expressivity and better fit the natural distribution of numeric values in running text.
Outcome: The proposed models outperform flow-based models on two numeric datasets in the financial and scientific domain.
Joint Effects of Context and User History for Predicting Online Conversation Re-entries (P19-1)

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Challenge: Existing methods for predicting online conversation re-entry focus on modeling engagement patterns in ongoing conversations or ignoring the rich information in users' previous chatting history.
Approach: They propose a neural framework with three main layers to model the conversation context and user history and their interactions with Twitter and Reddit to predict whether a user will return to a conversation they once participated in.
Outcome: The proposed framework outperforms the state-of-the-art methods on two large-scale Twitter and Reddit conversations, and achieves an F1 score of 61.1 on Twitter conversations.
Context versus Prior Knowledge in Language Models (2024.acl-long)

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Challenge: Existing studies have investigated how often a model will rely on prior knowledge over conflicting contextual information in answering questions.
Approach: They propose two mutual information-based metrics to measure a model’s dependency on a context and on its prior about an entity.
Outcome: The proposed metrics show that language models can integrate prior knowledge and new information in a predictable way across different questions and contexts.
Survival text regression for time-to-event prediction in conversations (2021.findings-acl)

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Challenge: a recent study has modelled time-to-event prediction tasks as classification tasks . authors: this is contrived and less informative than traditional classification models .
Approach: They propose to frame time-to-event prediction tasks as classification tasks . they use survival regression techniques commonly used in healthcare and reliability engineering .
Outcome: The proposed models outperform text regression methods and comparable classification models on three datasets.
In-Context Learning (and Unlearning) of Length Biases (2025.naacl-long)

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Challenge: Existing work has demonstrated the ability of large language models to learn lexical and label biases in-context negatively impacts performance and robustness of models.
Approach: They investigate the impact of length biases on in-context learning by analyzing model length information in-constext.
Outcome: The proposed model learns length biases in the context window without parameter updates.
Are Emergent Abilities in Large Language Models just In-Context Learning? (2024.acl-long)

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Challenge: Large language models have been claimed to acquire certain capabilities without having been specifically trained on them.
Approach: They propose a theory that explains emergent abilities by taking into account their potential confounding factors and rigorously substantiate this theory through over 1000 experiments.
Outcome: The proposed theory proves that emergent abilities are not truly emergental, but result from a combination of in-context learning, model memory, and linguistic knowledge.
Improving Backchannel Prediction Leveraging Sequential and Attentive Context Awareness (2024.findings-eacl)

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Challenge: Backchannels are short and often affirmative or empathetic responses from a listener during a conversation . et al. (2010) showed that timely backchanneling can enhance storytelling ability .
Approach: They propose a context-aware backchannel prediction approach that leverages a pretrained wav2vec model to enhance backchannel performance.
Outcome: The proposed approach improves performance in Korean and English datasets . it leverages the pretrained wav2vec model for encoding audio signal .

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